Measuring Expert Performance at Manually Classifying Domain Entities Under Upper Ontology Classes

Both oral and written communication is influential and prevalent in modern societies. This research study focused on interpersonal communication practices in a business context. The aim was to determine whether youths between 18 and 23 years of age undertaking their six-month period of internship as novice employees were adequately prepared to meet the demands of the workplace. Data were collected from the employers at organisations that employed novice employees. This provided critical perspectives on the competency of young people to cope with the communication demands of the workplace. Quantitative and qualitative methods of data collection were used. Sixty eight (68) employers completed the questionnaires. Interviews were also conducted with six randomly selected employers at the organisations where the young people were undertaking their respective internships. The findings show that workplace communication is complex and that many young people struggle to meet the challenges of communicating adequately in the workplace. Deliberate practice is fundamental to the development of communication skills and expert performance in the workplace. Recommendations are made to better prepare young people to face the challenges and demands of the dynamic workplace.

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A hierarchy of expert performance as applied to forensic anthropology

Journal of Forensic Sciences ◽

10.1111/1556-4029.14761 ◽

2021 ◽

Author(s):

Stephanie Hartley ◽

Allysha Powanda Winburn

Keyword(s):

Forensic Anthropology ◽

Expert Performance

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Implementation of a deep learning model for automated classification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) in real time

Scientific Reports ◽

10.1038/s41598-021-89365-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Song-Quan Ong ◽

Hamdan Ahmad ◽

Gomesh Nair ◽

Pradeep Isawasan ◽

Abdul Hafiz Ab Majid

Keyword(s):

Deep Learning ◽

Aedes Aegypti ◽

Real Time ◽

Aedes Albopictus ◽

Automated Classification ◽

Expert Performance ◽

Deep Convolutional Neural Networks ◽

Significant Difference ◽

Set Up

AbstractClassification of Aedes aegypti (Linnaeus) and Aedes albopictus (Skuse) by humans remains challenging. We proposed a highly accessible method to develop a deep learning (DL) model and implement the model for mosquito image classification by using hardware that could regulate the development process. In particular, we constructed a dataset with 4120 images of Aedes mosquitoes that were older than 12 days old and had common morphological features that disappeared, and we illustrated how to set up supervised deep convolutional neural networks (DCNNs) with hyperparameter adjustment. The model application was first conducted by deploying the model externally in real time on three different generations of mosquitoes, and the accuracy was compared with human expert performance. Our results showed that both the learning rate and epochs significantly affected the accuracy, and the best-performing hyperparameters achieved an accuracy of more than 98% at classifying mosquitoes, which showed no significant difference from human-level performance. We demonstrated the feasibility of the method to construct a model with the DCNN when deployed externally on mosquitoes in real time.

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